
STFNet-Filter: This model integrates the proposed STFNet component and the DeepSense structure. Within the STFNet component, we use the STFNet-filtering operation designed in Section REF . The intuition of DeepSense structure is to first perform local processing within each sensor and then perform global sensor fusion over multiple sensors. In this model, we replace all convolutional layers used in local/global sensor data processing with our time-frequency analyzing component, STFNet. Since our model has already incorporated time-domain analysis within the STFNet component through multi-resolution processing, we replace the Gated Recurrent Units (GRU) with simple feature averaging time at last.

STFNet-Conv: This model is almost the same as the STFNet-Filter, except that we use the STFNet-convolution operation designed in Section REF .

DeepSense-Freq: This model is the original DeepSense [1]}. It divides the input sensing data into chunks, and processes each chunk with DFT. It treats the real and imagery parts of discrete Fourier transformed time chunks as the additional feature dimensions. This is the state-of-the-art deep learning model for sensing data modelling and IoT applications.
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DeepSense-Time: This model is almost the same as the DeepSense-Freq, except that it directly takes the chunked raw sensing data without DFT as input.

ComplexNet: This model is a complex-value neural network [2]} that can operate on complex-value inputs.
Instead of using simple CNN and RNN structure as originally proposed [2]}, we cheat in their favor by using the DeepSense structure, which improves the performance in all tasks.
The network inputs are chunked sensing data with DFT.

